WO2011026257A1 - System and method for analyzing gait by fabric sensors - Google Patents

System and method for analyzing gait by fabric sensors Download PDF

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Publication number
WO2011026257A1
WO2011026257A1 PCT/CN2009/000999 CN2009000999W WO2011026257A1 WO 2011026257 A1 WO2011026257 A1 WO 2011026257A1 CN 2009000999 W CN2009000999 W CN 2009000999W WO 2011026257 A1 WO2011026257 A1 WO 2011026257A1
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WO
WIPO (PCT)
Prior art keywords
gait analysis
sensor according
fabric
sensor
fabric sensor
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PCT/CN2009/000999
Other languages
French (fr)
Chinese (zh)
Inventor
杨章民
杨子琳
杨景雯
杨皓
Original Assignee
Yang Changming
Yang Tzulin
Yang Chingwen
Yang Hao
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Yang Changming, Yang Tzulin, Yang Chingwen, Yang Hao filed Critical Yang Changming
Priority to PCT/CN2009/000999 priority Critical patent/WO2011026257A1/en
Priority claimed from CN201080039602.8A external-priority patent/CN102781319B/en
Publication of WO2011026257A1 publication Critical patent/WO2011026257A1/en

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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/1036Measuring load distribution, e.g. podologic studies
    • A61B5/1038Measuring plantar pressure during gait
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/68Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802Sensor mounted on worn items
    • A61B5/6804Garments; Clothes
    • A61B5/6807Footwear
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/112Gait analysis

Abstract

A system for analyzing gait by fabric sensors comprises: a socks sensing system, which comprises socks and at least one fabric sensor for sensing posture or action of a body; and a microprocessor, which receives signals from the fabric sensors. A method for analyzing gait by fabric sensors comprises detecting signals, in respect of body posture or action changes, generated by the socks sensing system and the microprocessor, and analyzing the detected signals to generate gait analysis parameters.

Description

 Gait analysis system and method using fabric sensor

 The invention can be applied to the fields of rehabilitation therapy, physical training, long-term care, orthopedics and sports medicine, health care, entertainment and the like. SUMMARY OF THE INVENTION The present invention is directed to sensing a walking motion of a wearer using a fabric sensor attached to the garment, and performing an analysis to know the physiological condition of the wearer. Background technique

 Gait analysis is often used to help athletes, as well as patients with impaired motor function, such as cerebral palsy, Parkinson's disease, stroke or accidental injuries. In the prior art, gait analysis is often performed in a specialized laboratory or physician's office, and must be accomplished using a number of sophisticated devices and complex methods. However, the most ideal gait analysis system should be able to be continuously monitored, low cost, easy to operate, and easy to obtain. The prior art also has a disadvantage: it does not exhibit the motor function of the subject in normal life. Therefore, both experts and patients need a low-cost system that can produce quantified and reproducible results. Most of the current gait analysis is used to help athletes and injured people, mainly in the laboratory, or in the doctor's office and visual observation. Clinicians rely on extensive gait analysis, diagnosis, and treatment, but they all face many complex factors. Gait analysis systems and procedures for general users should be readily monitored and inexpensive, making them easy to use and easy to obtain. However, traditional gait analysis equipment usually requires field testing or a comprehensive and robust gait analysis experiment in the laboratory, which makes the gait analysis system unfavorable.

Because the threshold of gait analysis is too high, the equipment with low price at this time can provide a large amount of data that can be read repeatedly and can monitor the gait signal of the user for a long time, especially for the injured user. Both patients with Parkinson's disease can be of great help. However, the current gait analysis related equipment has high barriers to entry, or the limitations of its related products, and there is no way to meet the needs of consumers, such as: US Patent Nos. US6789331 and US7168185, the patent contents are The shoe is used as a gait analysis sensor and cannot be washed, which causes inconvenience to the user. US Patent No. US6231527 is equipped with a camera and shoes as a gait analysis sensor, and when performing gait analysis, it can only be performed indoors, allowing users to perform gait analysis indoors, thereby causing users Inconvenient operation is not conducive to the promotion of gait analysis systems. US Patent No. US6984208 uses ultrasonic waves to test the user's posture and movement state and gait analysis related data, but it is not conducive to the popularity of gait analysis related systems due to the cost of acquiring ultrasonic related equipment. U.S. Patent No. US 2008010891 3A 1 uses a pressure sensor to detect a user's gait analysis, but still needs to have an independent power supply on each shoe or sock and is not a digital sensor, and at the same time, in signal processing Need feedback method (Feedback) For signal analysis, the process is too cumbersome, lengthy, and complex. It requires the use of neuro-fuzzy theory. It is difficult to synchronize the gait changes of the tester in real time. US Patent No. US20090012433A1 requires a camera, a microphone, and a sensor to detect the user's gait analysis related data, but this analysis method is too cumbersome, which is not conducive to the promotion of gait analysis. US Patent No. US200610282021. A1 uses a sensor and a remote monitoring system to detect the user's posture and gait analysis related data, but the system has a distance limitation when the user is far from the monitor. The monitor cannot process related messages. US Patent No. US2007 / 0112287 A1 uses an accelerometer and a gyroscope to hang on the ear to detect the user's gait analysis related data, which is not comfortable to use. Summary of the invention

 People spend most of their lives wearing clothes, sitting on a chair or lying on a bed, so they set up gait sensors on their trousers, socks, and clothes. The gait sensor can connect a sense of physiology. Detectors, such as heartbeat, breathing, body temperature, sweat, blood oxygen, electrocardiogram and other sensors, can sense physiological functions during limb movement, allowing the invention to be further extended to every level of daily life, and measured The gait analysis of the user in various postures is performed to analyze the physiological state of the user. Previously, the sensor was placed on the shoe and did not directly match the foot. The resulting gait analysis error was extremely large and could not be matched in various shoes, which was too expensive and consumes electricity. The invention is provided with the sensor on the sock, which is comfortable and washable on the one hand, and can measure the data of the gait analysis when the user wears different shoes, and is suitable for the users of all levels, because The size required for the socks is not as precise as the shoes, but the socks can fit snugly on the user's feet, so the resulting gait analysis can be more precise. In the present invention, the sock sensor can also know that when the user is walking, the user wears different shoes, and the gait analysis signal can be used to know the style of the shoe that the user is currently wearing. Such as: high heels, flat shoes, slippers, sports shoes, skates, etc. And the different gait analysis signals of different shoes are different, so the influence of the shoes worn by the user on the gait and the joints of the user can be evaluated. For example: If you feel uncomfortable wearing high heels, the user will have a backache. The sock sensor of the invention can be arranged on different shoes, and is easy to use and ergonomic for the user. As long as the user wears the sock sensor, it can be applied to various shoes, so that the growth time can be integrated. Continuous exercise physiology and gait analysis changes are very helpful for the health and safety of users. Moreover, since the present invention is to mount a sensor in one or more everyday clothes that are in contact with the body, it is advantageous for the promotion and application of the present invention. This technology has been approved by the IEEE EMBC 2009 Annual Conference and will be published in September, entitled "A wi re l es s ga it ana l ys is sys tem by di ta tex tile sensor s." Finally, this invention Not only for humans, but also for animals, such as: cats, dogs, behavior patterns can also be long-term monitoring analysis and predictive behavior patterns.

One of the objects of the present invention is to use clothes and pants in addition to the sensors on the socks. On the sensor, measure gait analysis and attitude changes, such as the angle of knee flexion, the length of the step, the number of steps per minute and the walking speed, or whether the heel is stepping on the ground, whether the arm is swinging, whether the waist is bent, and using The order of the posture changes, the period and other parameters, to observe the health of the user's limbs or rehabilitation treatment, or to determine the user's posture (such as walking forward, going backwards, running, going up the stairs, going down the stairs, Climbing, downhill, traversing, falling), can also be used as an input to interactive computer games, rather than virtual games that are now only on the computer, because the players themselves have no actual action to interact with the game software. It can also be used to detect the posture of the user when driving (for example: the degree of bending of the foot when the brake is applied). The invention is a wearable gait analysis system, which has the following features: 1. It is wearable, comfortable and can be directly installed on general pants or socks for use in real life; 2. The wearable type The gait analysis system has the following features: washable, durable, reliable, flexible, and inexpensive, so it can be easily applied to every level of daily life; 3. Use common digital output interfaces, such as: Bluetooth, The measured data can be directly transmitted to the instruments commonly used in daily life for signal analysis, such as: PDA or laptop. Therefore, these easy-to-obtain electronic instruments can be used to test the user's posture and gait analysis data, and can also predict the next action to generate and evaluate gait or behavioral abnormalities, and the variability and stability of each parameter. The degree can also be clearly expressed in the Power Spectrum. BRIEF DESCRIPTION OF THE DRAWINGS

 BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a block diagram of a gait analysis system utilizing a fabric sensor of the present invention.

 Fig. 2 is a view showing the sensor architecture of the first embodiment of the gait analysis system using the fabric sensor of the present invention.

 Figure 3A is a sensor position map on the sock.

 Figure 3B is a schematic illustration of the relative position of the sensor on the sock.

 Figure 4A is a sensor position map on the knee joint.

 Fig. 4B is a schematic view showing the position of the tension sensor mounted on the pants.

 Figure 5 is a typical gait timing diagram and sensor position.

 Figure 6 is the first four phases of the phase of ga i t analysis of the gait.

 Figure 7 is the post three phase of the phase of the gait (pha s e of ga i t) analysis.

 Figure 8A is a completed gait analysis diagram.

 Figure 8B is a flow chart of the method of the phase of the gait.

 Figure 9 is a schematic diagram for analysis of time parameters (Tempora l Parameters).

 Figure 10A is a pressure center analysis diagram of a normal walk.

 Figure 1 0B is a quality center analysis diagram of normal walking.

 Figure 10C is an analysis of the pressure center and mass center of the upper floor.

Figure 10D is an analysis of the pressure center and center of the running. Figure 1 0E is an analysis of the pressure center and mass center of the lower building.

 Figure 1 OF is the full pressure analysis of the first jump and then jump.

 Figure 1 0G is an analysis of the posture state of the first jump and then jump.

 Figure 1 0H is the full motion quality analysis diagram of the first jump and then jump.

 Figure 1 1 is a timing diagram of the running gait.

 Figure 12 is a timing diagram of the forward walking gait.

 Figure 1 3 is a timing diagram of the reverse gait.

 Figure 14 is a timing diagram of the gait upstairs.

 Figure 15 is a timing diagram of the gait of the lower building.

 Figure 16 is a schematic illustration of a first sock sensing system.

 Figure 17 is a schematic illustration of a second sock sensing system.

 Figure 18 is a schematic illustration of a third sock sensing system.

 Figure 19A is a circuit diagram of a resistor mounted in parallel next to the sensor.

 Fig. 19B is a circuit diagram of a resistor mounted in series next to the sensor.

 Figure 20 is a timing diagram of the Cavaliers walking.

 Figure 21 is a timing diagram of a rider riding a bicycle.

 Figure 22 is a schematic illustration of a pressure sensor outputted in multiple stages.

 Figure 23 is a schematic illustration of the time difference between the two sensors on the heel to observe the inner and outer touchdown. Fig. 24A is a timing chart for traverse to the left.

 Fig. 24B is a timing chart for traversing to the right.

 Figure 25 is a timing chart of the kick step.

 Figure 26 is a timing chart of sitting down after going forward.

 Figure 27 is a timing chart of the fall after the advancement.

 Figure 28 is a schematic diagram of estimating the walking speed using the time difference of the sensor.

 Figure 29 is a timing chart of the treadmill (speed setting is 2km/hr).

 30A and 30B are schematic diagrams of detection.

 Figure 31 is a flow chart of gait analysis.

 32A and 32B are schematic diagrams of posture discrimination. The best way to achieve your invention

 In order to further explain the technical means and efficacy of the present invention for achieving the intended purpose of the present invention, a specific embodiment of a gait analysis system using a fabric sensor according to the present invention will be described below with reference to the accompanying drawings and preferred embodiments. , structure, characteristics and their efficacy, as detailed below.

The system architecture of the present invention is shown in the system architecture diagram. Several switches, pressure, gravity or tension sensors are installed on the socks or pants depending on the application (refer to PCT/CN2008/001570, PCT/CN2005/001520, PCT/ CN2008/001571 patent application), the above sensor is a Available in elastic materials such as: metal (eg iron), non-metallic (eg rubber, silicone, foam) and conductive carbon (eg graphite). In addition, other elastic materials can be added to the fabric during the manufacturing process (eg rubber, foam, silicone, sponge, spring, cotton, elastic fiber).

(Spandex) > Synthetic elastic fiber (lycra), Synthetic Oak (SBR, Styrene Butadience Rubber), and foam-based materials to increase its elasticity. These fabric sensors are wired to the input of the microcontroller. When the sensor senses a change in attitude, a digital signal is generated to the microcontroller, and the microcontroller includes a program processing module that encodes the digital signals output by the respective sensors simultaneously for analysis, display, storage, or warning, or It is then transmitted by the communication module to other personal digital devices, such as smartphones or computers, for analysis, display, storage or warning.

 The fabric sensor can be connected to a physiological sensor, such that when the wearer moves, the fabric sensor is reacted by an external force, and the physiological sensor simultaneously senses the wearer's physiological signal, especially when the wearer moves When the vehicle is stopped, for example, when standing, lying down, when the posture of the test user and the gait are not changed, the physiological sensor is used to sense the physiological signal of the wearer to detect the state of the user.

 Microcontrollers can also be used to connect camera accelerometers or gyroscopes, camera accelerometers or gyroscopes to clothing, shoes, socks, control boxes or cell phones to increase the correctness of sensing limb movements.

 First preferred embodiment

As shown in the sensor architecture diagram of the first embodiment of the present invention, the present invention installs four digital sensors under the socks of the soles of the feet. When the external force is greater than 200 grams, the output is logically "1". " becomes "0", as shown in Figure 3, the sensor position map on the sock, Figure 3 is the position map relative to the sole of the foot, where (12) is the sole of the foot, (11) is the side of the foot, and (10) is the tibia. (9) is the part of the thumb of the foot. In addition, in order to get more accurate gait information, we installed two tension sensors on the upper part of the trousers of the trousers, respectively switching their output logic state at about 45 and 60 degrees, as shown in Figure 4Α, Figure 4 is the tension. A schematic representation of the sensor on the trousers. When the general health user walks forward, the timing diagrams of the digital sensor output logic states are as shown in FIG. 5, wherein the sensors 1 to 4 are tension sensors, and the sensors 5 to 12 are pressure sensors. In Figure 5, the first step of the two legs is the sensor 3 (45 degrees to the right knee), which changes from "0" to "1". At this time, the right leg is starting to rise, so the four sensors on the right foot start successively. Off-ground (sensors 12 to 9 change from logic "0" to "Γ"), and the sensors on the left foot are landed one after the other (sensors 8 to 5 are changed from logic "1" to "0"). Raise the right leg to the higher sensor 4 (60 degrees to the right knee) "0" Switch "Γ, the right foot is completely off the ground (sensors 9 to 12 are all "1"), the left foot is completely touched (sensing The 5 to 8 are both "0") and the left knee is straight (the sensors 1 to 2 are both "0"). Then the right leg begins to lay down the right foot and begins to land, so that the sensors 12 to 9 change from "1" to "0", while the left leg starts to raise the left foot and start to leave the ground, causing the sensors 8 to 5 to continue. Change from "0" to "1". At the same time, the knees of the left leg start to rise, and the sensors 1 and 2 change from "0" to "1", so that the left and right legs alternate, the present invention can obtain the gait timing diagram of FIG. 5, and the following diagram can be used to perform the following analysis. Generally, the gait timing is divided into seven phases, with the right heel touches the ground as the starting point, followed by the load response.

(loading response), mid-stance, terminal stance pre-swing, initial swing, mid-swing, terminal swing. The first four; f mesh is the stance phase, and the stance phase is 5, the invention can be completed with the digital sensors of the toes and heels of the feet (sensor 5, 8 , 9, 12), as shown in Figure 6, take (a) and (f) for the right heel touch (initial contact), (b) for the left toe off the ground, (c) for the right heel off the ground, (d) The left heel touches the ground, (e) touches the ground with the left toe. From the sensors 5, 8, 9, 12, we can measure (a) to (b) as the loading response, and (b) to (c) as the mid-stance, (c) To (d) is 3⁄4 terminal stance, (d) to (e) pre-swing, and (e) to (f) are swing periods (detailed in the next paragraph). The time of the first four phases shown in Figure 6 is 0.09, 0.23, 0.20, 0.62 seconds. At the same time, you can know the double foot touch (Double support), the time required for the standing and swinging periods of each foot, and the proportion of the whole pace.

 The latter three phases are referred to as the swing phase. For the latter three phases, the invention can be accomplished with four pull digital sensors on the knees of the legs (sensors 1, 2, 3, 4), as shown in Fig. 7. . In theory, the initial swing should start from the right foot off the ground (g) to the right knee's most bend point (h). In the present invention, the right knee 60 degree pull force digital sensor (sensor 4) The intermediate point (h, ) whose output is "1" is replaced. Therefore, from sensors 1, 2, 3, 4, we can measure (g) to (h, ) for initial swing, (h,) to (i) for mid-swing. (where (i) is the point at which the 45-degree sensor changes from "1" to "0"), and (i) to (f) are terminal swings. The time of the last three phases shown in Figure 7 is 0.12, 0.21, 0.09 seconds. The stance phase and the swing phase are integrated into a complete gait analysis map, as shown in Figure 8A.

 According to the above, the microcontroller reads the logic state of each sensor at a sampling frequency of 100 times per second, that is, a sufficiently high time resolution can measure the time occupied by each stage of the gait, wherein all the gaits The parameters and their examples can be presented. The flow chart of the method with the right foot as an example is shown in Figure 8B, and the same method is used for the left foot.

 First, zero the timer at the beginning;

 Wait until the right heel touches the ground to start timing (A);

 Wait until the left toe is off the ground (b), the recording time is the load response, and then the timer is reset to zero and then started;

 Wait for the right heel to leave the ground, record the time for the middle of the standing, then start the timer after zeroing; wait for the left heel to touch the ground, record the time for the end of the standing, then turn the timer back to zero and start; wait for the right toe off the ground, The recording time is the pre-swing period, and then the timer is reset to zero; then the right knee is 60 degrees, the tension sensor output is the intermediate point of 1, the recording time is the initial swing, and then the timer is reset to zero and then started;

Take the right knee 45 degrees, the pull sensor output changes from 1 to 0, and the recording time is the midpoint of the swing. After the timer is reset to zero, it is started;

 Wait for the right heel to touch the ground, and record the time as the end of the swing;

 Then repeat the entire process described above.

 The phase periods of each step of the same person may be more or less different. The invention can continuously record the phase periods of each step in a few minutes, obtain the average value and standard deviation of each parameter, and also know the average value and standard deviation of the two-leg support, the standing period, the swing period. If the standard deviation of a person is too large, it means that the person may have an injury to the motor function. This is an important indicator, and the present invention can be completed at a low cost and with a simple operation. In addition, the microcontroller can also predict the next gait by this gait change data. If the two gaits change greatly, it means that the user's balance is poor, or the road is uneven, for example, on a treadmill. Or on the suspension bridge, or the leg is injured or the shoes are not fit. Under normal circumstances, the gait of the left and right feet should be periodic, otherwise it may be a fall or other sudden situation, and the present invention can raise an alarm.

 Tempora l Parameters analysis

 Str ide l ength, Cadence and Wa l king speed are three important interrelated time parameters. From the timing chart, we can easily calculate the number of steps per minute. (cadence). As for the stride length (str ide l ength), it can be obtained by actually measuring the distance traveled by the user and dividing by the number of steps, or by the user himself, or by statistically checking the height or leg length. The average step length is set. The number of steps per minute (Cadence) is multiplied by the length of the step (St der Length) to obtain the wa lking speed. First let the user walk ten meters freely, he used 16 steps, then the available step length is 10/16=0.625 meters. Then, the number of steps is measured by the timing chart, as shown in Fig. 9. The time for taking the right heel touches the ground five times is 5.27 seconds, and the number of steps per minute is 60*2* (5/5. 27) = 113. 8 t imes /mi n (Because each time the right heel touches the ground, the right and left foot steps are taken, so the number of steps per minute is calculated as 60*2). The walking speed is multiplied by the length of the step by the number of steps per minute, that is, 0. 625* 113. 8 = 71. 125 m / sec (change to m / sec), one step length (str ide leng th) is two steps (s Tep length)„

 Centra l of pressure (COP) ^ Center of Mass analysis

The gait timing diagram clearly illustrates the sequence of each sensor switch, but for analysts who want to analyze a large amount of gait information, the timing diagram is not easy to navigate. Therefore, the present invention specifically defines a pressure center (Centra l of pressure, COP) and a center of mass analysis method, so that analysts can quickly and easily analyze a large amount of gait information. The timing diagram generated by the two-legged digital sensor is shown in Figure 10A, where (a) shows that the left foot is everywhere ί3⁄4· and the right foot is off the ground, and then (b) shows that the left foot is halfway away from the toes. Touching the ground with the foot, then (c) more than (b) one of the right ankle bone touches the ground. The change of the pressure center shows the stability of a person's walking gait. For example: Even if the user does not move on both feet, The pressure center still changes with time, so it can be known The sense of balance and the ability of the brain to control the feet. In the invention, the number of sensors touching the ground of the left foot is defined as positive, and the number of sensors touching the ground of the right foot is defined as negative, and the sum of the two can be approximated to represent the person, and the center of mass of the body is left or biased. Right, see Figure 1 0B ((a), (b), (c), (d), (e) in Figure 10B and 10A all represent the same moment) The pressure and mass center analysis of the upper floor can be seen, and As can be seen in the figure, for example, when the left foot completely touches the ground and the right foot is completely off the ground, the two add up to +4, which means that the center of the body shield is left. When both feet are completely touched, the sum of the two is 0, which means that the center of the body mass is in the middle. The graph of the center of mass and quantity changes with time. It can also analyze whether the gait of the person is normal and regular, such as drinking. The change in its quality center is completely irregular.

 Total pressure (tota l pressure), posture state (pos ture s tate), and total motion quality (tota l movement mas s) analysis

 The above-mentioned quality center analysis is very helpful for the action of the forward and backward and the ups and downs of the two feet. However, in some cases, it is impossible to distinguish, for example: the action of the two feet at the same time, all in the quality center Both are considered to be "0" and cannot be distinguished. Therefore, the present invention defines a total pressure (tot a l pressure) posture state (pos ture s ta te), and total motion quality (tota l movement mas s) analysis method, as follows:

 * Full pressure: The total number of sensors on the sole of the foot, regardless of the left and right feet, regardless of positive or negative, is positive;

* Posture state: The sensor on the left side of the body is set to be positive when it is changed by an external force, and the sensor on the right side is set to be negative when it is changed by an external force, and the posture state is the sum of all the sensors;

 • Full motion quality: The total number of sensors on the sole of the foot plus all the sensors on the body (such as the knee or elbow). These sensors are set to positive when they are changed by external force;

 We illustrate these three definitions with the attitude changes presented by first squatting and then jumping. The full pressure, posture state, and full motion mass analysis diagrams are shown in Figure 10F, 10G, 10H, respectively, where Figure 10F is full pressure. Analyze the graph, which shows that when the feet are flat, the total pressure is 4, when the arm is ready to jump, the heel is off the ground, and the total pressure is 2; finally, when jumping, both feet are off the ground. The value is zero, the time from 0 in the figure is the total time to jump to the ground, but the analysis chart formed by the center of mass (com) cannot be distinguished; then in the posture state analysis diagram 10G, we can see almost Both are at "0", and we set the value to 6 , which means that the subject is kneeling and ready to jump, so the process of jumping up first straightens the knee joint, then lifts the heel, then the toes are off the ground, so The value is zero. In this process, it can be seen that the knees of the legs are the time taken by the full motion quality analysis graph "6", so each parameter can express different posture states.

 Gait analysis of running, going up and down stairs

For the upstairs, running, downstairs, you can also get the pressure center (C0P) and quality center (COM) map, see Figure 10C, 10D, 10E. Figure 10C shows the pressure and mass center analysis of the running and can be seen from the figure, a to h is the time point of the gait analysis of the upper floor; a point is the signal of the right foot just stepping on the ladder of the upper floor, It is also the starting point of this analysis definition, and the vertical line drawn from this time point can be It is clear that the knee of the right foot is bent more than 60 degrees at this point in time, while the left knee is barely bent; b is the signal that the left foot just left the ground, and the left foot has just bent more than 45 degrees. The angle is less than 60 degrees, c time is the signal that the left knee flexion is just greater than 60 degrees. At this time, the right knee is still greater than 60 degrees, but the right knee is currently in a state of returning from a large angle to a small angle. , d time point is the time point when the right foot knee has just returned to a small angle. The e point is the signal when the left heel just stepped on the step. At this time, the left foot knee angle is greater than 60 degrees, and the right foot knee is still less than 45 degrees. The state of degree, point f is the signal when the right heel has been off the ground and the knee just bends more than 60 degrees, g point is the signal of the whole foot off the ground, h point is the signal that the right foot just stepped on the ladder upstairs It is also the end point of this analysis definition. From the signal points a to h, a cyclic analysis action of a complete upstairs gait can be known. The entire analysis paradigm is based on the right foot for analysis. The time from point a to point b is the time when the first foot supports the body; the time from point b to point e is the time when the right foot supports the body alone; the time from point e to point g is the second time that the feet support the body. Time; g to h time is the time when the right foot swings in the air.

 Figure 1 0D shows the pressure ^ ^ center analysis of the lower floor, and it can be seen from the figure that a to e is the time point of the running gait analysis; a point is the right heel just stepped on the ground The signal, which is also the starting point for this analysis definition, can be clearly seen from the vertical line drawn at that point in time, the knee of the right foot is less than 45 degrees, and the left foot is completely suspended, while the left foot is knee It is just bent more than 60 degrees; b is the signal that the right toe has just left the ground. At this time, the right knee has just bent more than 60 degrees, while the left knee is still at 60 degrees, but is about to recover to less than 45 degrees. The state of c; the time point is that the left heel has just stepped onto the ground, and the left knee is more than 45 degrees less than 60 degrees, the right knee is still greater than 60 degrees, and the time difference between point b and point c is It is the time when the feet are still in the air, because there will be a movement similar to a small jump when running; d is the time point when the left toe is about to leave the ground, the left knee just bends more than 60 degrees while the right knee is still Then at a 60 degree angle, To return to the state is in a less than 45 degrees, e signal point is right heel just stepped on the ground, but also defined the end of this analysis. At this time, the knee angle of the right foot is less than 45 degrees, and the left knee is still at a state greater than 60 degrees, and the time difference between the point d and the point e is the time when the feet are still in the air.

In Fig. 1E, a to f are the time points of the gait analysis of the downstairs; point a is the signal that the right toe has just stepped on the stairs downstairs, and is also the starting point of this analysis definition, from this point of time The vertical line drawn can clearly see that the knee bending angle of the right foot is less than 45 degrees at this time point, while the left foot knee is bent more than 60 degrees; b point is the signal that the right heel has just stepped into the lower stairs, right now The knees of the feet are still less than 45 degrees, and the knees of the left foot are still more than 60 degrees. The c point is the signal that the left toes are off the stairs. The right knees are just bent more than 60 degrees, but the right foot is still on the stairs. d The time point is the time when the left toe has just stepped into the stairs downstairs. At this time, the left knee is less than 45 degrees, the right knee is greater than 60 degrees, and the e point is the signal when the right toe just leaves the lower stairs. The knee angle is greater than 60 degrees, the left knee is still less than 45 degrees, and the f point is just the right toe. The signal to the stairs downstairs is also the end point of this analysis definition.

 The simplified running gait is shown in Figure 1. Compared with the normal walking, the standing period (A) is reduced and the swing period (B) is increased, and the time (C) at which the legs touch the ground at the same time is very short, in Figure 1. I can hardly see it in 1. If the clothes on the arm have sensors, the user's exercise physiology can be further analyzed. Under normal circumstances, the greater the swing of the hand, the faster the movement of the foot and the synchronization of the two, generally the left hand and the right foot are synchronized. The right hand is synchronized with the left foot. When the speed is faster, the elbow joint is more curved, which can be used to assist the gait analysis and the accuracy of the exercise physiology. It is easier to judge the user's posture change.

 The simplified front walking gait timing is shown in Figure 12.

 The simplified reverse gait is shown in Figure 13. The phase change is reversed compared to normal walking. The simplified upstairs gait is shown in Figure 14. It is significantly different from normal walking. For example, when the left leg is starting to go upstairs, the left knee is bent more than 45 degrees instead of straight (please refer to point (a) in Figure 14). ), this is to go up the stairs. The right foot touches the next step is the heel (Fig. 13 (b)), and the same right knee bends more than 45 degrees. On the other hand, the time difference between the heel and the thumb landing is small, the two are almost at the same time, and the knee is bent about twice as long as walking on the flat.

 The simplified stair gait is shown in Figure 15. It is also significantly different from normal walking. For example, Figure 15 (a) is the signal that the right foot just stepped on the next step at the end of the swing period, not the heel. First, while the left knee is bent more than 45 degrees, the left foot is in contact with the next step and the toe (b) first touches the ground. In addition, we found that when the upper and lower floors were used, the "60" generated by the knee 60-degree sensor was longer than the "1" generated in the behavior of going back and forth on the flat ground, so when the knee bend was greater than 45. When the time spent is greater than the level walking, the user is going up or down the stairs or going up and down. In addition, if the threshold value of the knee sensor is larger, the inclination of the up and down building or the up and down slope that can be detected is more easily detected without misjudgment, for example, 60 degrees response is 45 degrees. The length of the reaction time is the same. The slope of the up and down slope or the upper and lower floors will have the same time when the 60 degree sensor indicates that it is more inclined than the 45 degree sensor. If the 75 degree sensor is on the knee, You can get higher response to the up and down stairs or up and down slope changes.

 Identifying the gait phase, walking, reversing, stepping up and down

 In summary, there are significant differences in the gait phase timings of the forward, reverse, and upper and lower steps. The present invention can identify the user as being walking, reversing, going up or down by checking A and B in the following table. Stairs, of course, the principle of up and down slopes is the same as that of the upper and lower floors, so the ground can be evaluated by the signal measured by the sensor.

 Table 1: Logic status table for walking, retreating, going up or down stairs

Figure imgf000012_0001
Right knee (60°) 0 1 0 0 1 0 0 0

 Right foot thumb 0 1 0 0 0 0 0 0

 Right heel 1 1 1 0 0 0 1 0

 Of course, considering various interference factors, the phase timings produced by each step are not as shown above. The present invention can mount more sensors on pants or socks or clothing to improve the accuracy of recognition. For example, if two sensors are installed on the hips of the pants, the sensors of the two-leg socks are "1", and the sensors of the two pants are "1", and the hip sensor is also "1". " , that means the user is sitting, and the height of the chair is greater than the length of the leg, causing the legs to hang without touching the ground. Because in the summer, the user wears shorts, the sensor of the fixed knee joint is replaced by a sensor in the thigh area or a trousers in the joint (hitj on it) sensor to replace the sensor. The leg movement of the person's action, of course, if on the trousers, all the positions are placed on the sensor to measure the gait, the accuracy is better. At this time, the sensor on the sock cannot be connected to the controller on the trousers or Mobile phone, so the sensor on the sock is combined with the shoe, as shown in Figure 16, where there are 4 conductive threads on the socks, a, a2, a3, a4 corresponding to the conductive, M, b2, b3, b4 on the shoes Material, when the heel touches the ground, al turns the two ends of bl on, so that the "1" state of bl becomes "0", and a microprocessor is provided on the shoe for analysis, display, storage, warning. Or send a signal out.

 The same is true for another sock. Wireless communication, such as RFID or video, can be used to transfer information to each other. It can also interact with a microprocessor such as a controller or mobile phone on the clothes, and finally interact with the external monitoring system using wireless transmission. .

 Of course, the sensor formed by the socks and the shoes can also be multi-stage. For example, as shown in FIG. 17, the socks have a semi-spherical conductive material, and the two sets of conductive wires bl, b2 are concentric on the inner lining of the corresponding shoes. The distance between the wires of bl is less than the distance between the wires of b2, so when the hind legs are pressed down, first the conductive material of al, such as conductive sand or conductive metal sheet, first turns on the wires at both ends of bl, when the heel continues down. When pressed, al turns on the wires at both ends of b2, so there is a two-stage extrusion performance at the same point on the heel, instead of the single switch or one-stage pressure sensor in front, and at the same time in socks and shoes. Multiple multi-segment sensors are installed in different places to sense the pressure change (cop) when the gait is performed. At this point, each point of the pressure center (COP) can exhibit different pressure changes, so the quality should be expressed. At the center (COM), we can see the dynamic change of the human quality center (COM), because the performance of each point is not a simple "0" change "Γ, but a weight, more can show the human centroid (COM ) moving around Of.

We can also use the multi-section pressure sensor composed of two separate conductive wires to form the conductive hemispheres of the socks with a separate bl, b2 for more accurate analysis, as shown in Figure 18, using the variable on the shoes. The electric group or the piezoelectric material or the variable capacitor cl replaces bl, b2. As shown in Fig. 18, the variable electric group cl is placed on the inside of the shoe and one end corresponds to the center position of the ball of the al hemisphere of the sock. When the pressure is measured at both ends of the group cl, the greater the pressure, the more contact the al hemisphere with the cl, causing the electrical groups at both ends of the measurement cl to decrease with the increase of gravity, and the value can be measured when cl is piezoelectric material or When changing the capacitor. At this point, each sensor gets an analog signal. In short, we separate the original sock sensor, some of which are in the shoes, such as the underline of the shoe and the bottom of the sock; or the sock camera speed gauge or the gyroscope To detect the acceleration and angular velocity of the action, to help me to detect the information more accurately.

 By summarizing the timing of the various gaits described above, the following rules can be obtained, and can also be used to identify the walking, retreating, going up the stairs or going down the stairs.

• Walking:

 1 In general, the walking is usually the heel first, so the heel signal will appear first than the toe.

2 When the heel is on the ground, the knee of the foot will be less than 45 degrees.

• Back:

 1 In general, the back is usually the tip of the toe first, so the toe signal must appear earlier than the heel signal.

 2 Signals with knees bent more than 60 degrees are usually closer to the toe signal.

• Go upstairs:

 1 Before the foot signal of the foot appears, the foot will have a signal that the knee bends more than 60 degrees.

2 When the pedal signal appears, the knee signal will remain above 60 degrees.

 3 The signal that the knee has just straightened will appear in the foot signal of the foot.

 4 Usually the heel touches the ground first, so the heel signal will appear first.

• Going downstairs:

 1 The tip of the toes will land first, so the toe signal will appear first.

 When the toe signal appears, the knee signal of the foot will be less than 45 degrees.

 3 The signal when the knee is just greater than 60 degrees will appear in the pedaling signal period of the foot. In addition, a physiological sensor such as heartbeat, body temperature, sweat, blood oxygen, electrocardiogram, blood pressure, and respiration can be attached to the clothing to connect with the fabric sensor, and the physiological function can also be sensed.

 Second preferred embodiment

In order to make the invention as washable and comfortable to wear as the general fabric, the present invention uses a flexible and washable stainless steel wire connection sensor and a microcontroller, that is, a stainless steel wire as a transmission line, a sock or a garment trouser as a circuit board. The stainless steel wire and the microcontroller are connected by a common snap or mother-and-pin buckle on the garment. Considering the comfort of the clothes, the stainless steel wire and the snaps or the mother and child buckles on the clothes should not be too much. If it is necessary to install a plurality of sensors in practical applications, the present invention can install a resistor beside each fabric sensor with a resistance ratio of 2, and then in series (Fig. 19B) or in parallel (Fig. 19A). This principle is similar to binary coding. As shown in the circuit of Figure 19B, the equivalent resistance of the four sensors can be 0, R, 2 R, 3R, 4R, 5R ... up to 1 5R, a total of 16 values, so It can guarantee that regardless of how the fabric sensors are switched, the equivalent resistances formed in series or in parallel are different, which can be resolved by the microcontroller after analog-to-digital conversion (a na 1 og- digi 1 conver si on) Logic of each fabric sensor State. This can greatly reduce the wire and snap or mother and child buckle.

 Third preferred embodiment

 In addition to analyzing the gait, the present invention can also be applied to a bicycle rider to calculate the pedal pedal number, using the tire radius R, and stepping into a 2 π Ι to estimate the moving distance and speed because of the time used. It is known by the processor. The invention is applied to a bicycle knight, which is equipped with a 40- and 90-degree digital sensor on both knee joints, and the timing chart obtained by the rider and the bicycle is shown in Fig. 20 and Fig. 21, respectively, and right 1 and The left 1 is a 40 degree angle sensor, and the right 2 and left 2 are 90 degree angle sensors. Since the knee joint does not bend more than 90 degrees when walking, the 90-degree digital sensors on both knees in Figure 20 are all "0", and only the 40-degree digital sensor switches. In the bicycle, both knees have at least 40 degrees of bending, so the 40-degree digital sensors on both knees in Figure 21 are both "0", only 90 degrees in the digital sensor switch, because when riding, The sole of the foot is still on the pedal of the bicycle, so it is in the state of turning on "0". Therefore, it is only necessary to use the sensor of the knee and set it at 90 degrees to react, so that it is possible to take or ride separately. Because the foot of the foot produces a knee signal _^ socks signal in the case of double ^ only bicycle riding. Therefore, the user's behavior status can also be distinguished. : When walking or riding a bicycle, the road condition necessarily affects the gait. The present invention uses a camera, an accelerometer or a gyroscope to measure the road condition and improves the accuracy of the gait recognition. For example, when a bicycle passes through a pothole or when a person suddenly falls, the accelerometer or gyroscope will get considerable acceleration (for example, above a gravitational acceleration) or an angle change, and the camera will also take a sharp change in the image. At this time, the microcontroller can Suspend the recognition of the gait to avoid false positives and record road conditions.

 Fourth preferred embodiment

A digital sensor can have three stages of output if needed, see Figure 22. The center of the digital sensor is a ball embedded in a ring-shaped conductive rubber or silica gel with a conductor under the cross, but no conductor in the middle. When the ball is lightly pressed, the lowest annular conductive rubber in the ball touches the lower conductor, but the higher annular conductive rubber in the ball does not touch the lower conductor, so only one set of conductors conducts; when the ball is heavily pressed, Two annular conductive rubbers in the ball will touch the lower conductor, so there are two sets of conductors conducting. When the weight is heavier, the three groups on the ball are all connected. Therefore, in the gait analysis, the same point as the heel is not only "0". Or the result of "1", there may be different pressure or force performance, for example, when the pressure is greater than 20 kg, the first group of the ball is turned on, and when the gravity is greater than 40 kg, the two groups of the ball are turned on, greater than 60 When the force of kilograms is reached, the three groups of the ball are all connected, which can show the analysis result of the gait. At the same time, the performance of the pressure center (COP) is more meaningful. At the same time, the pressure change can be exhibited at each point of FIG. 10A, for example, 0 when there is no external force, 1 for the pressure at 20 to 40 kg, and 40 for the pressure. At a weight of 60 kg, the weight is 2, and when the pressure is greater than 60 kg, the weight is 3, and the value presented at the heel has 4 variations instead of "0" or "1". The Quality Center (COM) is even more meaningful, because the meaning of the center of mass (COM) or pressure center (COP) is not just the change in the sole of the subject when looking at gait analysis, but also the difference in the sole of the foot. Point in the gait cycle The situation of pressure changes. Therefore, when performing mass center (COM), total pressure (tota l pressure), posture state (pos ture ta te), and total motion quality (tota l movement mas s) analysis, each point is weighted (for example, pressure In 40 to 60 kg, the weight is 2). In addition, we can also get the impulse change from ί F*△ t = MV, where F is the force, M is the user mass, V is the speed, and A t is the action time. The result is that F* A t is the impulse. For example, when the foot is on the ground, the heel will add gravity, from 0 to 60 kg. As mentioned above, in this change time, the force on the heel is The time changes, causing the three-stage pressure sensor to change accordingly, so the product of the external force and time can be obtained, that is, the impulse, so it is not just the analysis of the pressure center, but also the time analysis map of the impulse.

 Fifth preferred embodiment

 More than two digital sensors can be mounted on the heel to distinguish the inside or outside of the foot when the foot is touched (except the inner eight or the outer eight feet), as shown in Figure 23. For normal people, the digital sensor of the two heels of the same foot has a time difference of less than a small range. If the two feet are too different, it may be caused by a foot injury or a lesion. The reason is that more sensors can be placed in the socks, and the gait analysis result we detected is not a straight line signal, but the performance of the overall foot gait analysis of the left and right feet as a solid plane.

 Sixth preferred embodiment

 The present invention can be implemented in a computer game in which the body interacts, and the body movements are input to the computer to increase the fun of the player. For example, the signal of the arm and the body is presented by the top. Some gaits that rarely occur in daily life can occur in the game, such as going left or right, see Figure 24A, Figure 24B. The four sensors on both feet touch the ground almost at the same time; for example, kicking See Figure 25. There are few bends on the knees, but the four sensors on the feet are almost; for example, sitting down, see Figure 26, bending both knees; for example, falling, common in old people or children, see Figure 27, feet four The sensors touch the ground at the same time. Therefore, the system can analyze the behavior patterns of users or animals; if there is danger, it can issue a warning. For such applications, you can add camera acceleration gauges or gyroscopes to your clothes, socks, shoes, control boxes, or mobile phones to increase the power of the game's analog power to compensate for the lack of digital sensing while at the same time. State analysis or exercise physiology can also increase accuracy.

 Seventh preferred embodiment

 When the invention is implemented, it is inevitable that it will encounter an unsatisfactory situation, such as the user does not wear the clothes when wearing clothes, pants or socks, or the clothes, pants or socks are deviated from the original position after strenuous exercise, causing the sensor signal to be wrong. The most common is the bounce, which is common in general mechanical switches, and is a pulse with a very short period (less than 0.1 second). In order to reduce malfunctions, the present invention considers the normal human condition and summarizes the following rules in order to pre-process the signals output by the respective sensors.

 1. When the large angle knee sensor is pulled apart, the small angle knee sensor must have been pulled apart;

2. With the inertia of the human body and the muscle strength of the general human body, the movements such as stretching the knees and lifting the pedals can not be completed in less than K seconds; 1 in the young people is 0.1, the elderly is 0. 15 , the old man The patient is 0.2 seconds

 According to the above rules, the procedure for pre-processing each sensor signal of the present invention is as follows:

 1. For the above positive and negative pulses with an action period less than K seconds, they are all eliminated;

 2. For smaller angle knee sensor, when the signal display is not pulled open and the knee sensor is pulled out at a large angle, the signal of the smaller angle knee sensor is modified to be opened. . ,

 The first preferred embodiment . : ,

The invention can use the time difference between the heel and the toe to touch the ground to estimate the speed of walking and obtain an approximate value. As shown in Figure 28, digital sensors (S2 and S1) are installed on the heel and toe. The distance between the two sensors is a certain value d. When the user moves forward at the speed V, we predict the sole of the foot. The ground contact velocity V' approximates the walking speed V, wherein the time difference between the two sensors S2 and S1 contacting the ground is At, then the velocity V' = d/At can be measured, and finally s (step lengh) = V . t+l/2at 2 can also be calculated. In addition, by V" = V, +at, where a is the acceleration, t is the time between the left foot and the right foot, V is the speed measured by the left foot, and V" is the next right foot touch. If so, the time t elapsed by the user from the left foot to the right foot can also be obtained, so that the acceleration a can be obtained, then s(step lengh) = V 0 t+l/2at ! 0 can be monitored by the upper The length, speed and acceleration of the foot and the foot change, so the variability of the length, speed and acceleration of the step can also be measured. The analysis of the information can also obtain the state of the user, if a more accurate speed is required. The user can record the time difference of at least two speeds on the treadmill at a constant speed, approaching by interpolation in practical applications, or using a camera accelerometer or gyroscope to assist in correcting the accuracy. Taking a timing diagram (Fig. 29) of a user walking on a treadmill (speed setting 2km/hr) as an example, the time difference between the two sensors S1 and S2 at the first to sixth steps is 0.32, 0.50. , 0.15, 0.35, 0.31, 0.30 seconds, the distance between the two sensors S1 and S2 is 20 cm, and the converted walking speed is 2.0, 1.28, 4.26, 1.83, 2.06, 2.13 km/hr, in addition, between these six steps The time required for each step is 0.8, 0.88, 0.57, 0.57, 1.15 seconds, so the acceleration of each step is -0.9, 3.39, -4.26, 0.4, 0.06Km / hr. sec corresponding to the calculated pace (step lengh) is 0.52, 0.67, 0.48, 0.34, 0.69 m. These measured accelerations a are, for example, the first step to the second step are -0.9, resulting in the acceleration values of the second to third steps being 3.39, by This can also be seen that the step added in the second step is 0.67, which is caused by the first step. Then, in the sixth step, we can see that the degree of addition is 0. It means that the subject has already responded to the speed of the step machine. The treadmill is synchronized. In gait analysis, we can use these parameters to judge whether a person's gait is stable. If the value changes too much, it may be a precursor to the fall, and it can provide a warning. On the contrary, we can also use it as a virtual game. Input.

For the angular velocity of the joint, it can also be evaluated. For example, if the knee joint is set at 45 degrees and 65 degrees, the angular velocity = 6 / t, where t is the time difference between the 45 degree and 60 degree sensor activation, L For the calf length, the Θ is 15 degrees, then L*6 can be the distance between 45 and 60 degrees. In addition, L*W can be used as the angular velocity of the knee joint, so we can also measure the attitude change during the swing period. Each parameter is used to assess the stability and variability of the subject.

 For the ankle joint, we can also place two sensors to detect the angle. For example, the two sensors on the heel and the side (S1 and S2) represent the heel strike (10 degrees) and the whole foot flat (0 At the time of the degree, the time difference At which the two sensors are activated can calculate the angular velocity of the ankle joint W=10 /At, as shown in Fig. 30A.

If it is on the uphill slope, it is no longer like walking on the ground, but is affected by the slope, so it can be estimated by the time difference. As shown in Fig. 30B, it is assumed that the foot is in a constant-speed circular motion between the heel touch and the toe touch ground, and in the normal walking condition, the heel touches the ground instantaneously, the sole of the foot is at an angle to the ground (10 degrees), and S1 and S2 are sensed. The time difference between the grounding and the grounding time is At, and the grounding gradient is Θ*(Δ - At' ) / Δ1 0

In short, there are two points Sl, S2 landing time difference can get the sole speed V. The borrowing measures the time difference ΔΠ, the right sole velocity V2, and the left sole velocity VI of the left and right feet. From V2 = Vl+al*Atl, the acceleration al between the two feet can be estimated; or the borrowing is measured. The time difference At2 between the left foot and the second foot and the next left foot velocity are V3. From V3=Vl+a2*At2, the speed a2 of the two grounds before and after the left foot can also be estimated. In principle Δί2 is approximately 2*Atl. From the above, the present invention can measure the acceleration during the movement, and the speed of each time, and then statistically obtain its variability. It is known from the variability whether the gait of the subject is stable and can be used to predict the gait of the next step, because the velocity remains stable when the acceleration and velocity variability are fixed. Furthermore, by the distance S=V*t+0.5*a*t 2 , when the speed, acceleration and time difference are both stable, the next walking distance S can be estimated. On the other hand, if the values of acceleration, speed, distance, etc. vary greatly, indicating that the subject's gait is abnormal, an alarm may have to be raised, such as a temporary fall or a collision with another person or thing.

 The same principle can be obtained from at least two angle sensors at the joint, and the angular velocity of the joint can be known. The angular acceleration can be obtained by using the value of this and the next angular velocity and the time difference. In such a case, if the angular acceleration or the angular variability is small, the angular velocity of the next joint motion can be predicted from the angular velocity of this time, and by L = R * e, where R is the length of the arm where the joint is located, The angle of change gives the length L of the swing. If the angular velocity and angular acceleration remain stable (ie, the variability is low), the swing length L can be predicted.

 Ninth preferred embodiment

The present invention can estimate the slope of the upslope or ups and downs of stairs by using the length of time the knee joint sensor is pulled apart, and obtain an approximate value. The steeper the slope, the higher the leg must be lifted, the more the knee joint is bent, and the longer the knee sensor is pulled apart. Of course, we can also set a multi-segment sensor on the trousers, for example: 45 degrees, 60 degrees, 75 degrees three segments, when the knee joint is straight from the beginning, only the 45 degree sensor has produced 'Τ', and the next 45 degrees and 60 degrees sensor are generated", if Even if the 75-degree sensor is "", it means that the angle of the knee is larger, which means that the steeper the slope.

 Tenth preferred embodiment

 In Fig. 31, the flow of the gait analysis, we know that the heel of the foot is also the first when the user advances, but if the ground is uphill, the time difference between the heel strike time and the toe landing time becomes shorter. Conversely, if it is downhill, the forefoot will land first and the downhill angle will be larger. The pressure distribution on the toe and the heel will be opposite, ie the pressure will move to the toes, which is like wearing high heels. If it is added to the posture change of the upper body, as shown in Figure 31, where A stands for, when the user's posture changes, the sensor also responds at the same time, and is received by the information provided by the various sensors. Turn on or off the relevant signal on/off; B stands for providing a database to compare the on/off related signals on or off to determine the user's posture change; The 3D stereoscopic information of the posture change made by the user at the same time can more accurately detect the change of the posture of the subject, and can also know the posture state of the person at that time, for example, Table 2.

 Table 2:

Figure imgf000019_0001

 Among them, the 8-bit string in the database from right to left represents right 腋, right elbow, left 腋, left elbow, right hip, right knee, left hip, left knee.

Claims

Rights request
WHAT IS CLAIMED IS: 1. A gait analysis system utilizing a fabric sensor, comprising: a sock sensing system comprising a sock and at least one fabric sensor that senses a posture or motion of the body;
 A microprocessor receives signals from the fabric sensor.
 2. A gait analysis system utilizing a fabric sensor according to claim 1 wherein said sock sensing system further comprises a shoe.
 3. The gait analysis system using a fabric sensor according to claim 2, wherein the sensor is installed in a sock and a shoe, wherein at least one conductive wire is mounted on the sock, and the corresponding position on the shoe Install the same amount of conductive material.
 4. The gait analysis system using a fabric sensor according to claim 2, wherein the sensor is mounted in a sock and a shoe, wherein the socks are mounted with a semi-spherical conductive material, and Two sets of concentric wires are mounted on the inner lining of the corresponding shoe.
 5. The gait analysis system using a fabric sensor according to claim 2, wherein the sensor is mounted in a sock and a shoe, wherein the socks are mounted with a semi-spherical conductive material, and the phase is A variable electric group, a piezoelectric material or a variable capacitor is mounted on the inner lining of the corresponding shoe.
 6. The gait analysis system using a fabric sensor according to claim 2, wherein the sensed parameter is a resistance value or a capacitance value.
 7. A gait analysis system utilizing a fabric sensor according to claim 1 wherein the microprocessor is capable of receiving signals from the fabric sensor and encoding and analyzing by the included program processing module.
 8. A gait analysis system using a fabric sensor according to claim 7 wherein the pressure center, mass center, full pressure, posture state, and full motion quality are generated by sensor sensing.
 9. A gait analysis system using a fabric sensor according to claim 7, wherein the speed of one foot, the acceleration, the length of the step produced by the speed or acceleration is generated.
 A gait analysis system using a fabric sensor according to claim 7, wherein an angular velocity, an angular acceleration, or a swing distance of the joint is generated.
 A gait analysis system using a fabric sensor according to claim 7, characterized in that the ground gradient can be estimated.
 2. The gait analysis system using a fabric sensor according to claim 7, wherein the program processing module preprocesses the digital output of each sensor to reduce noise interference by the following rules. For positive and negative pulses with a period less than K seconds, they are all eliminated.
The gait analysis system using the fabric sensor according to claim 7, wherein the program processing module preprocesses the digital output of each sensor to reduce noise interference by the following rules. For the small-angle knee sensor, when the signal display is not pulled apart and the knee sensor is pulled apart, the small-angle knee sensor signal is modified to be pulled apart.
14. The gait analysis system using a fabric sensor according to claim 1, wherein the fabric sensor is a tension, pressure, gravity or force sensor, and the output signal is a digital signal.
A gait analysis system using a fabric sensor according to claim 1, wherein said fabric sensor is further coupled to a physiological sensor.
 16. The gait analysis system using a fabric sensor according to claim 1, wherein said program processing module is a signal of a sensor of a right heel, a right toe, a left heel, and a left toe. Trigger point to calculate gait parameters.
 1 . The gait analysis system using a fabric sensor according to claim 1, comprising: at least two fabric sensors capable of sensing a posture or an action of the body, and each fabric sense The detectors are each connected in series or in parallel with a resistor to connect the analog to digital converter with two wires for the microcontroller to read the logic state of each fabric sensor.
 18. The gait analysis system using a fabric sensor according to claim 1, comprising: having a small angle and a large angle tension sensor on each of the two knees, the small angle tension sensor being The bending of the knee joint within 30 to 50 degrees changes the output state, preferably 40 degrees. The large angle tension sensor changes the output state within the bending of the knee joint from 80 degrees to 100 degrees, preferably 90 degrees.
 19. The gait analysis system using a fabric sensor according to claim 1, wherein said sensor detects an overall foot gait analysis of a two-dimensional plane.
 20. A gait analysis system using a fabric sensor according to claim 19, wherein the impulse of the body is sensed.
 21. A gait analysis system using a fabric sensor according to claim 1 or 8, wherein said sensor is a multi-segment digital sensor.
 22. A gait analysis system utilizing a fabric sensor according to claim 1 wherein the posture or motion of the body is sensed.
 23. A gait analysis system utilizing a fabric sensor according to claim 1 wherein said sock sensing system further comprises a camera, an accelerometer, or a gyroscope to increase accuracy.
 24. The gait analysis system using a fabric sensor according to claim 1, wherein said microprocessor is coupled to a camera, an accelerometer, or a gyroscope for detecting a road surface condition.
 25. A gait analysis method using a fabric sensor, comprising: detecting a signal of a body posture or motion change generated by a sock sensing system and a processor, analyzing the obtained signal to generate a step State analysis parameters.
 26. A method of gait analysis using a fabric sensor according to claim 25, wherein the generated gait analysis parameters provide a pressure center, a mass center, a full pressure, a posture state, and a full motion quality.
 27. A gait analysis method using a fabric sensor according to claim 25, characterized in that
28. A method of gait analysis using a fabric sensor according to claim 25, wherein the generated gait analysis parameters produce a speed, an acceleration of one foot, a length of a step generated by speed and acceleration.
29. A method of gait analysis using a fabric sensor according to claim 25, wherein the generated gait analysis parameters are indicative of a ground slope.
 30. A method of gait analysis using a fabric sensor according to claim 25, wherein the program processing module for generating the gait analysis parameter is a sense of a right heel, a right toe, a left heel, and a left toe. The signal of the detector is the trigger point to calculate the gait parameters.
 31. A gait analysis method using a fabric sensor according to claim 25, characterized in that
32. The gait analysis method using a fabric sensor according to claim 25, wherein the generated gait analysis parameter can derive an impulse.
 33. The gait analysis method using a fabric sensor according to claim 25, wherein the generated gait analysis parameter can derive a variability of each parameter.
PCT/CN2009/000999 2009-09-03 2009-09-03 System and method for analyzing gait by fabric sensors WO2011026257A1 (en)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103211604A (en) * 2013-05-07 2013-07-24 河北工程大学 Sitting-position psychological state detecting device and detecting method
CN104027107A (en) * 2014-05-23 2014-09-10 浙江大学 Wearable electrocardio measurement device
CN105388495A (en) * 2014-08-20 2016-03-09 博能电子公司 Estimating local motion of physical exercise
CN105476639A (en) * 2015-11-20 2016-04-13 南京中科创达软件科技有限公司 Electronic equipment for detecting and correcting splayfeet

Families Citing this family (87)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8647287B2 (en) 2008-12-07 2014-02-11 Andrew Greenberg Wireless synchronized movement monitoring apparatus and system
US8880358B2 (en) * 2010-04-16 2014-11-04 Thomas J. Cunningham Sensing device
CN103442607B (en) * 2011-02-07 2016-06-22 新平衡运动公司 For monitoring the system and method for athletic performance
JP5728689B2 (en) * 2011-05-17 2015-06-03 株式会社国際電気通信基礎技術研究所 Walking signal generation device and walking signal generation system
FR2976700B1 (en) * 2011-06-17 2013-07-12 Inst Nat Rech Inf Automat Method for generating command coordination control orders for displacing an animated platform and corresponding generator.
US9524424B2 (en) 2011-09-01 2016-12-20 Care Innovations, Llc Calculation of minimum ground clearance using body worn sensors
US9165113B2 (en) * 2011-10-27 2015-10-20 Intel-Ge Care Innovations Llc System and method for quantitative assessment of frailty
US9339691B2 (en) 2012-01-05 2016-05-17 Icon Health & Fitness, Inc. System and method for controlling an exercise device
US9078478B2 (en) * 2012-07-09 2015-07-14 Medlab, LLC Therapeutic sleeve device
US8983637B2 (en) 2012-07-30 2015-03-17 Mapmyfitness, Inc. Determining authenticity of reported fitness-related activities
US9877667B2 (en) 2012-09-12 2018-01-30 Care Innovations, Llc Method for quantifying the risk of falling of an elderly adult using an instrumented version of the FTSS test
US9351900B2 (en) 2012-09-17 2016-05-31 President And Fellows Of Harvard College Soft exosuit for assistance with human motion
WO2014071051A1 (en) * 2012-10-31 2014-05-08 Mapmyfitness, Inc. System and method for personal and peer performance ranking of outdoor activities
US9700241B2 (en) 2012-12-04 2017-07-11 Under Armour, Inc. Gait analysis system and method
KR101428325B1 (en) * 2012-12-27 2014-08-07 현대자동차주식회사 Robot foot apparatus
JP5811360B2 (en) * 2012-12-27 2015-11-11 カシオ計算機株式会社 Exercise information display system, exercise information display method, and exercise information display program
WO2014104360A1 (en) * 2012-12-28 2014-07-03 株式会社東芝 Motion information processing device and method
JP6507099B2 (en) 2013-01-21 2019-04-24 カラ ヘルス,インコーポレイテッドCala Health,Inc. Wearable device
US20160235363A9 (en) * 2013-03-04 2016-08-18 Polar Electro Oy Computing user's physiological state related to physical exercises
RU2531689C1 (en) * 2013-03-05 2014-10-27 Общество С Ограниченной Ответственностью "Хилби" Method for monitoring individual's motion load and inner sole for implementing it
RU2531697C1 (en) * 2013-03-05 2014-10-27 Общество С Ограниченной Ответственностью "Хилби" Method for determining individual's weight and inner sole for implementing it
EP2969058A4 (en) 2013-03-14 2016-11-16 Icon Health & Fitness Inc Strength training apparatus with flywheel and related methods
EP2783630A1 (en) * 2013-03-27 2014-10-01 ETH Zurich Human motion analysis method and device
US8736439B1 (en) * 2013-04-06 2014-05-27 Kenneth Feng Shinozuka Sock for bed-departure detection
TWI562100B (en) * 2013-05-03 2016-12-11 Ind Tech Res Inst Device and method for monitoring postural and movement balance for fall prevention
JP6359343B2 (en) * 2013-07-01 2018-07-18 キヤノンメディカルシステムズ株式会社 Motion information processing apparatus and method
US20150068069A1 (en) * 2013-07-27 2015-03-12 Alexander Bach Tran Personally powered appliance
JP5888309B2 (en) * 2013-10-31 2016-03-22 カシオ計算機株式会社 Training support apparatus and system, form analysis apparatus and method, and program
US9445769B2 (en) * 2013-12-06 2016-09-20 President And Fellows Of Harvard College Method and apparatus for detecting disease regression through network-based gait analysis
US9443063B2 (en) 2013-12-06 2016-09-13 President And Fellows Of Harvard College Method and apparatus for using gait analysis to determine a health quality measure
US9403047B2 (en) 2013-12-26 2016-08-02 Icon Health & Fitness, Inc. Magnetic resistance mechanism in a cable machine
US9801568B2 (en) * 2014-01-07 2017-10-31 Purdue Research Foundation Gait pattern analysis for predicting falls
WO2015120186A1 (en) 2014-02-05 2015-08-13 President And Fellows Of Harvard College Systems, methods, and devices for assisting walking for developmentally-delayed toddlers
US20150254992A1 (en) * 2014-03-06 2015-09-10 Maneesh SETHI Memory-enhancing and habit-training methods and devices
US10433612B2 (en) 2014-03-10 2019-10-08 Icon Health & Fitness, Inc. Pressure sensor to quantify work
JP6606105B2 (en) 2014-06-02 2019-11-13 カラ ヘルス,インコーポレイテッドCala Health,Inc. System and method for peripheral nerve stimulation for treating tremor
TWI559144B (en) * 2014-06-05 2016-11-21 zhi-zhen Chen Scale - guided limb rehabilitation system
CN106470739B (en) 2014-06-09 2019-06-21 爱康保健健身有限公司 It is incorporated to the funicular system of treadmill
US9288556B2 (en) * 2014-06-18 2016-03-15 Zikto Method and apparatus for measuring body balance of wearable device
WO2015195965A1 (en) 2014-06-20 2015-12-23 Icon Health & Fitness, Inc. Post workout massage device
JP6080078B2 (en) * 2014-08-18 2017-02-15 高知県公立大学法人 Posture and walking state estimation device
CA2960003C (en) * 2014-09-04 2018-04-17 Kunihiko Kaji Information terminal device, motion capture system and motion capture method
KR20170045746A (en) * 2014-09-18 2017-04-27 쿠니히로 시이나 Recording device, mobile terminal, analysis device, program, and storage medium
WO2016089466A2 (en) * 2014-09-19 2016-06-09 President And Fellows Of Harvard College Soft exosuit for assistance with human motion
CN104287743B (en) * 2014-10-23 2016-03-23 东南大学 A kind of integration of the multi-joint angle based on flexible fabric serial detection system
KR20160075118A (en) * 2014-12-19 2016-06-29 한국산업기술대학교산학협력단 System for Estimating the Center of Pressure in Gait Rehabilitation Robots and method thereof
US10258828B2 (en) 2015-01-16 2019-04-16 Icon Health & Fitness, Inc. Controls for an exercise device
US20160253891A1 (en) * 2015-02-27 2016-09-01 Elwha Llc Device that determines that a subject may contact a sensed object and that warns of the potential contact
US10391361B2 (en) 2015-02-27 2019-08-27 Icon Health & Fitness, Inc. Simulating real-world terrain on an exercise device
CN104799862B (en) * 2015-04-02 2018-06-19 中国海洋大学 A kind of human body is unbalance method for early warning and system
TWI554266B (en) * 2015-04-24 2016-10-21 Univ Nat Yang Ming Wearable gait rehabilitation training device and gait training method using the same
US10248188B2 (en) * 2015-06-03 2019-04-02 James M. O'Neil System and method for generating wireless signals and controlling digital responses from physical movement
US9836118B2 (en) 2015-06-16 2017-12-05 Wilson Steele Method and system for analyzing a movement of a person
JP6554996B2 (en) * 2015-08-17 2019-08-07 トヨタ自動車株式会社 Walking training apparatus and walking training method thereof
US20180140225A1 (en) * 2015-09-21 2018-05-24 Figur8, Inc. Body part deformation analysis using wearable body sensors
US9978247B2 (en) 2015-09-24 2018-05-22 Microsoft Technology Licensing, Llc Smart fabric that detects events and generates notifications
TWI582389B (en) * 2015-10-26 2017-05-11 行政院原子能委員會核能研究所 Navigation system for inertial positioning with gait detection method
EP3442417A1 (en) * 2016-01-07 2019-02-20 Gunther Röder Method and device for detecting a fall
TWI615129B (en) * 2016-02-19 2018-02-21 財團法人資訊工業策進會 Gait analysis system and method thereof
US10493349B2 (en) 2016-03-18 2019-12-03 Icon Health & Fitness, Inc. Display on exercise device
US10293211B2 (en) 2016-03-18 2019-05-21 Icon Health & Fitness, Inc. Coordinated weight selection
US10561894B2 (en) 2016-03-18 2020-02-18 Icon Health & Fitness, Inc. Treadmill with removable supports
US10272317B2 (en) 2016-03-18 2019-04-30 Icon Health & Fitness, Inc. Lighted pace feature in a treadmill
CA3022913A1 (en) * 2016-05-04 2017-11-09 Allen Selner Instrumented orthotic
US10252109B2 (en) 2016-05-13 2019-04-09 Icon Health & Fitness, Inc. Weight platform treadmill
US10441844B2 (en) 2016-07-01 2019-10-15 Icon Health & Fitness, Inc. Cooling systems and methods for exercise equipment
US10471299B2 (en) 2016-07-01 2019-11-12 Icon Health & Fitness, Inc. Systems and methods for cooling internal exercise equipment components
MX2019000460A (en) * 2016-07-13 2019-10-02 Palarum Llc Patient monitoring system.
US10213134B2 (en) * 2016-08-18 2019-02-26 Timothy W. Markison Wireless in-shoe physical activity monitoring implementation
US10500473B2 (en) 2016-10-10 2019-12-10 Icon Health & Fitness, Inc. Console positioning
US10376736B2 (en) 2016-10-12 2019-08-13 Icon Health & Fitness, Inc. Cooling an exercise device during a dive motor runway condition
US10207148B2 (en) 2016-10-12 2019-02-19 Icon Health & Fitness, Inc. Systems and methods for reducing runaway resistance on an exercise device
DE102016220660B4 (en) * 2016-10-21 2019-03-07 Robert Bosch Gmbh Device, control unit, electric bicycle and method for detecting a movement sequence of a two-wheeled driver, as well as for controlling a motor
TWI646997B (en) 2016-11-01 2019-01-11 美商愛康運動與健康公司 Distance sensor for positioning the console
JP6508174B2 (en) * 2016-11-29 2019-05-08 カシオ計算機株式会社 Running analysis device, running analysis method and running analysis program
TW201821131A (en) 2016-12-05 2018-06-16 美商愛康運動與健康公司 Offsetting treadmill deck weight during operation
US10272293B2 (en) * 2016-12-23 2019-04-30 Industrial Technology Research Institute Body motion analysis system, portable device and body motion analysis method
KR101924441B1 (en) * 2017-01-26 2018-12-03 주식회사 제윤메디컬 Method, Apparatus and System for measuring body balance using Smart Insole
KR101909743B1 (en) * 2017-01-26 2018-10-18 주식회사 제윤메디컬 Method and system for controlling electrical equipment for vehicles based on Smart Insole
KR101856077B1 (en) * 2017-03-21 2018-05-09 스피나 시스템즈 주식회사 Walking Calibrator Using Smart Insole
US20180360350A1 (en) * 2017-06-16 2018-12-20 Medstar Health Systems and methods for injury prevention and rehabilitation
WO2019022533A1 (en) * 2017-07-28 2019-01-31 충남대학교 산학협력단 Smart shoes system for determining walking state of wearer
WO2019027182A1 (en) * 2017-07-31 2019-02-07 충남대학교 산학협력단 Smart shoe system for calculating energy expenditure
KR101899129B1 (en) * 2017-11-07 2018-10-31 전자부품연구원 Apparatus and method for analysis of exercising posture using textile sensor
WO2019163714A1 (en) * 2018-02-26 2019-08-29 国立大学法人お茶の水女子大学 Movement determination device, movement determination system, movement determination method, and program
US10325472B1 (en) * 2018-03-16 2019-06-18 Palarum Llc Mount for a patient monitoring device
ES2736261A1 (en) * 2018-06-25 2019-12-27 Univ Coruna Procedure, control module and product of computer program to control a system to measure the amount of scroll displacement of a subject

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001039655A2 (en) * 1999-12-06 2001-06-07 Trustees Of Boston University In-shoe remote telemetry gait analysis system
WO2008058048A2 (en) * 2006-11-06 2008-05-15 Colorado Seminary, Which Owns And Operates The University Of Denver Smart apparatus for gait monitoring and fall prevention
US20080146968A1 (en) * 2006-12-14 2008-06-19 Masuo Hanawaka Gait analysis system
CN201135440Y (en) * 2007-07-24 2008-10-22 中国科学院理化技术研究所 Minitype measurement device for recording the human walking information

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN2040365U (en) * 1988-08-20 1989-07-05 中国人民解放军北京军区总医院 Inertia walking state tester
US5546955A (en) * 1992-06-18 1996-08-20 Wilk; Peter J. Medical stocking for temperature detection
US6922592B2 (en) * 2000-04-04 2005-07-26 Medtronic, Inc. Implantable medical device controlled by a non-invasive physiological data measurement device
US20070068244A1 (en) * 2003-10-17 2007-03-29 M.B.T.L. Limited Measuring forces in athletics
JP4374596B2 (en) * 2004-01-30 2009-12-02 学校法人同志社 Load measurement method
US7771371B2 (en) * 2004-08-11 2010-08-10 Andante Medical Devices Ltd Sports shoe with sensing and control
US8028443B2 (en) * 2005-06-27 2011-10-04 Nike, Inc. Systems for activating and/or authenticating electronic devices for operation with footwear
DE102005055842A1 (en) * 2005-11-23 2007-05-24 Alpha-Fit Gmbh Pressure sensor for incorporation in clinical test socks or stockings incorporates pressure-sensitive threads or ribbons
US8188868B2 (en) * 2006-04-20 2012-05-29 Nike, Inc. Systems for activating and/or authenticating electronic devices for operation with apparel
US7726206B2 (en) * 2006-11-02 2010-06-01 The Regents Of The University Of California Foot pressure alert and sensing system
CN200994779Y (en) * 2006-12-06 2007-12-26 国家体育总局体育科学研究所 Human-body gait motor measuring shoes and its energy consumption realtime monitor

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001039655A2 (en) * 1999-12-06 2001-06-07 Trustees Of Boston University In-shoe remote telemetry gait analysis system
WO2008058048A2 (en) * 2006-11-06 2008-05-15 Colorado Seminary, Which Owns And Operates The University Of Denver Smart apparatus for gait monitoring and fall prevention
US20080146968A1 (en) * 2006-12-14 2008-06-19 Masuo Hanawaka Gait analysis system
CN201135440Y (en) * 2007-07-24 2008-10-22 中国科学院理化技术研究所 Minitype measurement device for recording the human walking information

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103211604A (en) * 2013-05-07 2013-07-24 河北工程大学 Sitting-position psychological state detecting device and detecting method
CN104027107A (en) * 2014-05-23 2014-09-10 浙江大学 Wearable electrocardio measurement device
CN105388495A (en) * 2014-08-20 2016-03-09 博能电子公司 Estimating local motion of physical exercise
CN105476639A (en) * 2015-11-20 2016-04-13 南京中科创达软件科技有限公司 Electronic equipment for detecting and correcting splayfeet

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